Overview

Dataset statistics

Number of variables22
Number of observations21597
Missing cells34132
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory169.0 B

Variable types

Numeric17
Categorical3
Boolean1
DateTime1

Alerts

bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 5 other fieldsHigh correlation
sqft_living is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
grade is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_above is highly overall correlated with bedrooms and 5 other fieldsHigh correlation
sqft_basement is highly overall correlated with sqft_livingHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
price is highly overall correlated with sqft_living and 3 other fieldsHigh correlation
floors is highly overall correlated with yr_builtHigh correlation
waterfront is highly overall correlated with viewHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly imbalanced (94.2%)Imbalance
view is highly imbalanced (72.4%)Imbalance
sqft_basement has 13279 (61.5%) missing valuesMissing
yr_renovated has 20853 (96.6%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique

Reproduction

Analysis started2023-10-13 14:54:48.420964
Analysis finished2023-10-13 14:55:29.287971
Duration40.87 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct21597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10799
Minimum1
Maximum21597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:29.397798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1080.8
Q15400
median10799
Q316198
95-th percentile20517.2
Maximum21597
Range21596
Interquartile range (IQR)10798

Descriptive statistics

Standard deviation6234.6612
Coefficient of variation (CV)0.5773369
Kurtosis-1.2
Mean10799
Median Absolute Deviation (MAD)5399
Skewness0
Sum2.33226 × 108
Variance38871000
MonotonicityStrictly increasing
2023-10-13T16:55:29.625932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
14397 1
 
< 0.1%
14405 1
 
< 0.1%
14404 1
 
< 0.1%
14403 1
 
< 0.1%
14402 1
 
< 0.1%
14401 1
 
< 0.1%
14400 1
 
< 0.1%
14399 1
 
< 0.1%
14398 1
 
< 0.1%
Other values (21587) 21587
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
21597 1
< 0.1%
21596 1
< 0.1%
21595 1
< 0.1%
21594 1
< 0.1%
21593 1
< 0.1%
21592 1
< 0.1%
21591 1
< 0.1%
21590 1
< 0.1%
21589 1
< 0.1%
21588 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3732
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:29.763709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92629889
Coefficient of variation (CV)0.27460539
Kurtosis49.821835
Mean3.3732
Median Absolute Deviation (MAD)1
Skewness2.0236412
Sum72851
Variance0.85802964
MonotonicityNot monotonic
2023-10-13T16:55:29.874032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.9%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 196
 
0.9%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
10 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 196
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.9%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.9%
3 9824
45.5%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1158263
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:30.005835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7689843
Coefficient of variation (CV)0.36344397
Kurtosis1.2793153
Mean2.1158263
Median Absolute Deviation (MAD)0.5
Skewness0.51970928
Sum45695.5
Variance0.59133685
MonotonicityNot monotonic
2023-10-13T16:55:30.130621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.5 5377
24.9%
1 3851
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1445
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (19) 641
 
3.0%
ValueCountFrequency (%)
0.5 4
 
< 0.1%
0.75 71
 
0.3%
1 3851
17.8%
1.25 9
 
< 0.1%
1.5 1445
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5377
24.9%
2.75 1185
 
5.5%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1034
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2080.3219
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:30.287869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11430
median1910
Q32550
95-th percentile3760
Maximum13540
Range13170
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation918.10613
Coefficient of variation (CV)0.44132889
Kurtosis5.252102
Mean2080.3219
Median Absolute Deviation (MAD)540
Skewness1.4732155
Sum44928711
Variance842918.86
MonotonicityNot monotonic
2023-10-13T16:55:30.474070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1024) 20302
94.0%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
390 1
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
470 2
< 0.1%
480 2
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9776
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15099.409
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:30.768099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800.8
Q15040
median7618
Q310685
95-th percentile43307.2
Maximum1651359
Range1650839
Interquartile range (IQR)5645

Descriptive statistics

Standard deviation41412.637
Coefficient of variation (CV)2.7426661
Kurtosis285.49581
Mean15099.409
Median Absolute Deviation (MAD)2618
Skewness13.072604
Sum3.2610193 × 108
Variance1.7150065 × 109
MonotonicityNot monotonic
2023-10-13T16:55:30.920355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 119
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9766) 19803
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
1
12583 
2
8396 
3
 
618

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12583
58.3%
2 8396
38.9%
3 618
 
2.9%

Length

2023-10-13T16:55:31.060630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-13T16:55:31.191918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 12583
58.3%
2 8396
38.9%
3 618
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 12583
58.3%
2 8396
38.9%
3 618
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12583
58.3%
2 8396
38.9%
3 618
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12583
58.3%
2 8396
38.9%
3 618
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12583
58.3%
2 8396
38.9%
3 618
 
2.9%

waterfront
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.2 KiB
False
21451 
True
 
146
ValueCountFrequency (%)
False 21451
99.3%
True 146
 
0.7%
2023-10-13T16:55:31.320216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
0
19485 
2
 
957
3
 
508
1
 
330
4
 
317

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19485
90.2%
2 957
 
4.4%
3 508
 
2.4%
1 330
 
1.5%
4 317
 
1.5%

Length

2023-10-13T16:55:31.423058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-13T16:55:31.558830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 19485
90.2%
2 957
 
4.4%
3 508
 
2.4%
1 330
 
1.5%
4 317
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19485
90.2%
2 957
 
4.4%
3 508
 
2.4%
1 330
 
1.5%
4 317
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19485
90.2%
2 957
 
4.4%
3 508
 
2.4%
1 330
 
1.5%
4 317
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19485
90.2%
2 957
 
4.4%
3 508
 
2.4%
1 330
 
1.5%
4 317
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19485
90.2%
2 957
 
4.4%
3 508
 
2.4%
1 330
 
1.5%
4 317
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
3
14020 
4
5677 
5
1701 
2
 
170
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Length

2023-10-13T16:55:31.678777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-13T16:55:31.818042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6579155
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:31.934866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1731997
Coefficient of variation (CV)0.15320092
Kurtosis1.135148
Mean7.6579155
Median Absolute Deviation (MAD)1
Skewness0.78823664
Sum165388
Variance1.3763975
MonotonicityNot monotonic
2023-10-13T16:55:32.044190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 8974
41.6%
8 6065
28.1%
9 2615
 
12.1%
6 2038
 
9.4%
10 1134
 
5.3%
11 399
 
1.8%
5 242
 
1.1%
12 89
 
0.4%
4 27
 
0.1%
13 13
 
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 27
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8974
41.6%
8 6065
28.1%
9 2615
 
12.1%
10 1134
 
5.3%
11 399
 
1.8%
12 89
 
0.4%
ValueCountFrequency (%)
13 13
 
0.1%
12 89
 
0.4%
11 399
 
1.8%
10 1134
 
5.3%
9 2615
 
12.1%
8 6065
28.1%
7 8974
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 27
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct942
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.5968
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:32.185451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation827.75976
Coefficient of variation (CV)0.4627984
Kurtosis3.4055198
Mean1788.5968
Median Absolute Deviation (MAD)450
Skewness1.4474342
Sum38628326
Variance685186.22
MonotonicityNot monotonic
2023-10-13T16:55:32.342702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (932) 19708
91.3%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
390 1
 
< 0.1%
410 1
 
< 0.1%
420 2
< 0.1%
430 1
 
< 0.1%
440 1
 
< 0.1%
460 1
 
< 0.1%
470 2
< 0.1%
480 4
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct302
Distinct (%)3.6%
Missing13279
Missing (%)61.5%
Infinite0
Infinite (%)0.0%
Mean741.92366
Minimum10
Maximum4820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:32.527402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile190
Q1450
median700
Q3980
95-th percentile1450
Maximum4820
Range4810
Interquartile range (IQR)530

Descriptive statistics

Standard deviation404.74104
Coefficient of variation (CV)0.54552923
Kurtosis3.4722104
Mean741.92366
Median Absolute Deviation (MAD)260
Skewness1.0966117
Sum6171321
Variance163815.31
MonotonicityNot monotonic
2023-10-13T16:55:32.674167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 217
 
1.0%
700 209
 
1.0%
500 209
 
1.0%
800 201
 
0.9%
400 184
 
0.9%
1000 148
 
0.7%
300 142
 
0.7%
900 142
 
0.7%
200 105
 
0.5%
750 104
 
0.5%
Other values (292) 6657
30.8%
(Missing) 13279
61.5%
ValueCountFrequency (%)
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 6
 
< 0.1%
80 20
0.1%
90 21
0.1%
100 42
0.2%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9997
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:32.825434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.375234
Coefficient of variation (CV)0.014903723
Kurtosis-0.65769443
Mean1970.9997
Median Absolute Deviation (MAD)23
Skewness-0.46944998
Sum42567680
Variance862.90438
MonotonicityNot monotonic
2023-10-13T16:55:32.982192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 453
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 420
 
1.9%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17313
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 453
2.1%

yr_renovated
Real number (ℝ)

MISSING 

Distinct69
Distinct (%)9.3%
Missing20853
Missing (%)96.6%
Infinite0
Infinite (%)0.0%
Mean1995.9288
Minimum1934
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:33.140438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1934
5-th percentile1964
Q11987
median2000
Q32007.25
95-th percentile2014
Maximum2015
Range81
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation15.599946
Coefficient of variation (CV)0.0078158831
Kurtosis0.97385984
Mean1995.9288
Median Absolute Deviation (MAD)10
Skewness-1.0652191
Sum1484971
Variance243.35831
MonotonicityNot monotonic
2023-10-13T16:55:33.297667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 73
 
0.3%
2013 31
 
0.1%
2003 31
 
0.1%
2007 30
 
0.1%
2000 29
 
0.1%
2005 29
 
0.1%
2004 22
 
0.1%
1990 22
 
0.1%
2009 21
 
0.1%
2006 20
 
0.1%
Other values (59) 436
 
2.0%
(Missing) 20853
96.6%
ValueCountFrequency (%)
1934 1
 
< 0.1%
1940 2
< 0.1%
1944 1
 
< 0.1%
1945 3
< 0.1%
1946 1
 
< 0.1%
1948 1
 
< 0.1%
1950 1
 
< 0.1%
1951 1
 
< 0.1%
1953 1
 
< 0.1%
1954 1
 
< 0.1%
ValueCountFrequency (%)
2015 14
 
0.1%
2014 73
0.3%
2013 31
0.1%
2012 8
 
< 0.1%
2011 9
 
< 0.1%
2010 15
 
0.1%
2009 21
 
0.1%
2008 15
 
0.1%
2007 30
0.1%
2006 20
 
0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.952
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:33.464899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.513072
Coefficient of variation (CV)0.00054561776
Kurtosis-0.85400486
Mean98077.952
Median Absolute Deviation (MAD)42
Skewness0.40532219
Sum2.1181895 × 109
Variance2863.6489
MonotonicityNot monotonic
2023-10-13T16:55:33.627152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 602
 
2.8%
98038 589
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 547
 
2.5%
98034 545
 
2.5%
98118 507
 
2.3%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16100
74.5%
ValueCountFrequency (%)
98001 361
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 141
 
0.7%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5033
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560093
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:33.797375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.5718
Q347.678
95-th percentile47.7497
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.13855177
Coefficient of variation (CV)0.0029131938
Kurtosis-0.67579021
Mean47.560093
Median Absolute Deviation (MAD)0.1049
Skewness-0.48552159
Sum1027155.3
Variance0.019196592
MonotonicityNot monotonic
2023-10-13T16:55:33.943639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6846 17
 
0.1%
47.5322 17
 
0.1%
47.6624 17
 
0.1%
47.6711 16
 
0.1%
47.6955 16
 
0.1%
47.6886 16
 
0.1%
47.6647 15
 
0.1%
47.6904 15
 
0.1%
47.686 15
 
0.1%
Other values (5023) 21436
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21398
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21597
Negative (%)100.0%
Memory size168.9 KiB
2023-10-13T16:55:34.104872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.231
Q3-122.125
95-th percentile-121.9798
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14072352
Coefficient of variation (CV)-0.0011514518
Kurtosis1.052123
Mean-122.21398
Median Absolute Deviation (MAD)0.101
Skewness0.88488948
Sum-2639455.4
Variance0.019803108
MonotonicityNot monotonic
2023-10-13T16:55:34.833215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 115
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20586
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.6203
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:34.993448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.23047
Coefficient of variation (CV)0.34492271
Kurtosis1.5917328
Mean1986.6203
Median Absolute Deviation (MAD)410
Skewness1.1068754
Sum42905039
Variance469540.8
MonotonicityNot monotonic
2023-10-13T16:55:35.138715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 180
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1720 166
 
0.8%
1800 166
 
0.8%
1620 164
 
0.8%
Other values (767) 19835
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8682
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12758.284
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:35.296974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2002.4
Q15100
median7620
Q310083
95-th percentile37045.2
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27274.442
Coefficient of variation (CV)2.137783
Kurtosis151.39566
Mean12758.284
Median Absolute Deviation (MAD)2505
Skewness9.524362
Sum2.7554065 × 108
Variance7.4389518 × 108
MonotonicityNot monotonic
2023-10-13T16:55:35.448224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 356
 
1.6%
6000 288
 
1.3%
7200 210
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
3600 111
 
0.5%
4500 111
 
0.5%
5100 109
 
0.5%
Other values (8672) 19582
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2023-10-13T16:55:35.596980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:35.751732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3622
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540296.57
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:35.917471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1322000
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7622000
Interquartile range (IQR)323000

Descriptive statistics

Standard deviation367368.14
Coefficient of variation (CV)0.67993794
Kurtosis34.541359
Mean540296.57
Median Absolute Deviation (MAD)150000
Skewness4.0233647
Sum1.1668785 × 1010
Variance1.3495935 × 1011
MonotonicityNot monotonic
2023-10-13T16:55:36.075213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 172
 
0.8%
350000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (3612) 20097
93.1%
ValueCountFrequency (%)
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
89000 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7060000 1
< 0.1%
6890000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110000 1
< 0.1%
4670000 1
< 0.1%
4500000 1
< 0.1%
4490000 1
< 0.1%

house_id
Real number (ℝ)

Distinct21420
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5804743 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-10-13T16:55:36.251430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1274039 × 108
Q12.1230492 × 109
median3.9049304 × 109
Q37.3089005 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858513 × 109

Descriptive statistics

Standard deviation2.8767357 × 109
Coefficient of variation (CV)0.6280432
Kurtosis-1.2607499
Mean4.5804743 × 109
Median Absolute Deviation (MAD)2.4025303 × 109
Skewness0.24322552
Sum9.8924503 × 1013
Variance8.2756084 × 1018
MonotonicityNot monotonic
2023-10-13T16:55:36.422656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795000620 3
 
< 0.1%
8910500150 2
 
< 0.1%
7409700215 2
 
< 0.1%
1995200200 2
 
< 0.1%
9211500620 2
 
< 0.1%
1524079093 2
 
< 0.1%
4305200070 2
 
< 0.1%
1450100390 2
 
< 0.1%
7893805650 2
 
< 0.1%
109200390 2
 
< 0.1%
Other values (21410) 21576
99.9%
ValueCountFrequency (%)
1000102 2
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

Interactions

2023-10-13T16:55:26.379244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:50.175151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.809406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.959064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.205453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.498787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.692649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.212608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.280292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.449503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.595246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.871530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.164522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:17.822058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.056728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.150369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.222512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:26.501048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:50.293962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.933208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.077887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.333743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.617585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.800985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.320934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.392301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.564826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.720537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.003819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.276842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:17.942366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.168061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.266183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.335339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:26.624350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:50.411781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.056511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.201677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.476515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.747375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.921283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.439759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.511110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.687121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.847841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.135094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.399146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.071160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.289853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.389986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.460130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:26.751657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:50.540565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.183320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.332443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.625783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.875170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:02.047106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.565542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.644897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.820907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.984612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.272383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.533442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.234896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.417167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.516285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.590420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:26.877444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:50.669857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.313100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.468238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.764049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.017443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:02.177871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.690841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.768698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.959185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.112352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.416651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.664720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.410116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.545443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.641593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.722709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.012737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:50.796164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.442890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.602011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.902538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.147746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:02.301673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.816153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.886508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.087479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.247126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.550929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.801501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.541905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.673738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.765384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.856992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.126545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:51.437618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.555228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.719825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.029333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.261565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:02.411007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.930957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.003821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.205789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.367932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.676727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.921318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.663711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.788559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.882195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.973819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.240372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:51.546931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.667032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.837135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.152136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.379361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:02.527321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.043785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.110150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.318118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.507210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.801534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.044125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.786014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:20.897378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.992518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.093616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.361169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:51.660749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.787838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:55.970421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.277936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.503664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:02.642636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.155596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.232953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.436421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.624521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:13.929820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.167415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:18.907161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.015707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.110130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.213934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.490461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:51.782054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:53.918128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.102709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.426197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.632955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.127361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.276402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.367738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.561228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.756309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.065102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.305194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.035959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.137994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.229938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.341717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.633234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:51.919835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.055409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.263451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.566971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.767739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.262634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.416690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.505029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.698998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:11.892602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.220853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.442986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.170239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.273286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.369215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.478996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.781994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.052130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.195185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.409715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.709242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:00.914503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.432858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.548975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.701220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.837789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.037695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.362139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.579753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.311013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.413052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.501003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.614290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:27.906812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.200383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.329382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.543501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.842030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.050286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.604084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.665777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.833500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:09.962577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.180956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.496411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.707549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.440307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.542853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.624969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.744071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:28.032106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.321190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.452685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.677798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:58.974317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.178590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.722394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.783091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:07.962783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.092380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.321739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.630197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.831647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.557618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.662162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.748272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.867386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:28.155894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.445001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.575680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.807079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.109601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.303389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.845697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:05.899902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.090577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.227660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.454036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.766977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:16.954949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.682917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.784957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.861091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:25.991683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:28.272720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.564314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.693492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:56.929881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.234707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.424186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:03.964017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.012235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.204904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.341972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.590985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:14.889966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:17.078252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.799231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:21.896776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:23.972414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:26.112979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:28.404890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:52.688101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:54.827788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:57.079642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:54:59.372476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:01.565355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:04.092800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:06.143022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:08.331192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:10.473266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:12.738230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:15.033734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:17.685278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:19.933515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:22.030063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:24.107695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-13T16:55:26.250450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-13T16:55:36.573427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idbedroomsbathroomssqft_livingsqft_lotgradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15pricehouse_idfloorswaterfrontviewcondition
id1.0000.0120.1130.044-0.1350.0940.069-0.0620.2290.0000.000-0.0040.0010.019-0.1300.0400.0050.2540.0140.0170.111
bedrooms0.0121.0000.5210.6480.2170.3800.5400.4810.1810.165-0.168-0.0220.1930.4440.2020.3440.0060.0530.0150.0380.012
bathrooms0.1130.5211.0000.7460.0690.6580.6910.3570.5680.241-0.2050.0080.2620.5710.0640.4970.0150.4370.1050.1100.122
sqft_living0.0440.6480.7461.0000.3050.7160.8430.7310.3530.104-0.2070.0310.2850.7470.2850.6440.0020.3030.1470.1470.056
sqft_lot-0.1350.2170.0690.3051.0000.1520.2730.363-0.037-0.169-0.319-0.1220.3710.3600.9220.075-0.1170.0190.0160.0400.040
grade0.0940.3800.6580.7160.1521.0000.7120.2720.5010.214-0.1820.1040.2230.6630.1570.6580.0200.3830.1230.1410.128
sqft_above0.0690.5400.6910.8430.2730.7121.0000.3480.472-0.006-0.279-0.0260.3860.6970.2550.5420.0040.4290.0810.0890.106
sqft_basement-0.0620.4810.3570.7310.3630.2720.3481.000-0.0250.171-0.138-0.0110.1770.4290.3460.338-0.0140.1330.1690.1530.097
yr_built0.2290.1810.5680.353-0.0370.5010.472-0.0251.0000.210-0.317-0.1260.4130.336-0.0160.1020.0270.5260.0330.0420.248
yr_renovated0.0000.1650.2410.104-0.1690.214-0.0060.1710.2101.0000.0420.030-0.038-0.011-0.1710.1720.0010.0960.1390.0710.221
zipcode0.000-0.168-0.205-0.207-0.319-0.182-0.279-0.138-0.3170.0421.0000.249-0.577-0.287-0.326-0.009-0.0050.2460.0740.0740.074
lat-0.004-0.0220.0080.031-0.1220.104-0.026-0.011-0.1260.0300.2491.000-0.1430.027-0.1160.456-0.0040.1810.0310.0680.057
long0.0010.1930.2620.2850.3710.2230.3860.1770.413-0.038-0.577-0.1431.0000.3810.3730.0640.0070.2310.0870.0840.081
sqft_living150.0190.4440.5710.7470.3600.6630.6970.4290.336-0.011-0.2870.0270.3811.0000.3660.5720.0000.2940.0870.1470.062
sqft_lot15-0.1300.2020.0640.2850.9220.1570.2550.346-0.016-0.171-0.326-0.1160.3730.3661.0000.063-0.1150.0160.0000.0350.013
price0.0400.3440.4970.6440.0750.6580.5420.3380.1020.172-0.0090.4560.0640.5720.0631.0000.0040.1450.3230.2050.023
house_id0.0050.0060.0150.002-0.1170.0200.004-0.0140.0270.001-0.005-0.0040.0070.000-0.1150.0041.0000.0590.0060.0290.030
floors0.2540.0530.4370.3030.0190.3830.4290.1330.5260.0960.2460.1810.2310.2940.0160.1450.0591.0000.0160.0230.246
waterfront0.0140.0150.1050.1470.0160.1230.0810.1690.0330.1390.0740.0310.0870.0870.0000.3230.0060.0161.0000.5700.018
view0.0170.0380.1100.1470.0400.1410.0890.1530.0420.0710.0740.0680.0840.1470.0350.2050.0290.0230.5701.0000.025
condition0.1110.0120.1220.0560.0400.1280.1060.0970.2480.2210.0740.0570.0810.0620.0130.0230.0300.2460.0180.0251.000

Missing values

2023-10-13T16:55:28.598079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-13T16:55:28.972478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-13T16:55:29.207601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idbedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15datepricehouse_id
0131.000118056501False0371180.000NaN1955NaN9817847.511-122.257134056502014-10-132219007129300520
1232.250257072422False0372170.000400.00019511991.0009812547.721-122.319169076392014-12-095380006414100192
2321.000770100001False036770.000NaN1933NaN9802847.738-122.233272080622015-02-251800005631500400
3443.000196050001False0571050.000910.0001965NaN9813647.521-122.393136050002014-12-096040002487200875
4532.000168080801False0381680.000NaN1987NaN9807447.617-122.045180075032015-02-185100001954400510
5644.50054201019301False03113890.0001530.0002001NaN9805347.656-122.00547601019302014-05-1212300007237550310
6732.250171568192False0371715.000NaN1995NaN9800347.310-122.327223868192014-06-272575001321400060
7831.500106097111False0371060.000NaN1963NaN9819847.410-122.315165097112015-01-152918502008000270
8931.000178074701False0371050.000730.0001960NaN9814647.512-122.337178081132015-04-152295002414600126
91032.500189065602False0371890.000NaN2003NaN9803847.368-122.031239075702015-03-123230003793500160
idbedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15datepricehouse_id
215872158832.500227055362False0382270.000NaN2003NaN9806547.539-121.881227057312014-08-255072507852140040
215882158932.000149011263False0381490.000NaN2014NaN9814447.570-122.288140012302015-01-264290009834201367
215892159042.500252060232False0392520.000NaN2014NaN9805647.514-122.167252060232014-10-146106853448900210
215902159143.500351072002False0392600.000910.0002009NaN9813647.554-122.398205062002015-03-2610100007936000429
215912159232.500131012942False0381180.000130.0002008NaN9811647.577-122.409133012652015-02-194750002997800021
215922159332.500153011313False0381530.000NaN2009NaN9810347.699-122.346153015092014-05-21360000263000018
215932159442.500231058132False0382310.000NaN2014NaN9814647.511-122.362183072002015-02-234000006600060120
215942159520.750102013502False0371020.000NaN2009NaN9814447.594-122.299102020072014-06-234021011523300141
215952159632.500160023882False0381600.000NaN2004NaN9802747.535-122.069141012872015-01-16400000291310100
215962159720.750102010762False0371020.000NaN2008NaN9814447.594-122.299102013572014-10-153250001523300157